Adaptive bandwidth allocation method based on network traffic prediction

Abstract

The present invention discloses an adaptive bandwidth allocation method based on network traffic prediction, which relates to the field of adaptive bandwidth allocation technology, and specifically includes the following steps: obtaining bandwidth demand information of each bandwidth device to be analyzed in real time, analyzing it, evaluating the bandwidth demand of each bandwidth device to be analyzed, and dividing each bandwidth device to be analyzed into high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices according to the evaluation results; according to the division results of each bandwidth device to be analyzed, constructing a bandwidth allocation mechanism, and taking different bandwidth allocation measures for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively. The present invention solves the problem of uneven bandwidth allocation when multiple devices are running in parallel in a smart home system, accurately identifies high and low demand devices, dynamically optimizes bandwidth allocation, and improves system stability and bandwidth resource utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of adaptive bandwidth allocation, and in particular to an adaptive bandwidth allocation method based on network traffic prediction. Background Art

[0002] Adaptive bandwidth is a technology that dynamically adjusts bandwidth resources according to changes in network traffic, avoiding the limitations of traditional static allocation methods. With the fluctuation and sudden increase of network traffic, a single fixed bandwidth can no longer meet the demand. Therefore, adaptive bandwidth allocation based on network traffic prediction is crucial. By predicting future traffic trends, bandwidth resources can be adjusted in advance to avoid congestion and waste, and ensure stable network performance. This method improves bandwidth utilization efficiency, reduces delays and packet loss, and improves user experience.

[0003] Existing adaptive bandwidth allocation technologies based on network traffic prediction usually monitor network traffic in real time and dynamically adjust bandwidth by combining historical data and traffic prediction models. First, the system collects data from various network nodes, such as traffic, delay, and packet loss indicators, and then uses machine learning or statistical analysis methods to predict traffic change trends within a certain period of time in the future. Based on the prediction results, the bandwidth allocation strategy will be automatically adjusted to prioritize bandwidth allocation to high-demand applications or traffic-intensive areas, while reducing bandwidth allocation during low-demand or idle periods to optimize the use of bandwidth resources. This technology usually relies on a feedback mechanism to continuously adjust bandwidth allocation to cope with rapid changes in traffic and ensure network stability and performance. For example, for high-bandwidth demand applications such as video streaming or real-time communication, the system will predict their future needs and increase bandwidth in advance to avoid freezes or delays caused by insufficient bandwidth.

[0004] The prior art has the following deficiencies:

[0005] In a smart home system, when multiple devices in the home are running at the same time, especially when multiple high-definition video surveillance cameras start to transmit real-time video, network traffic will increase dramatically. At the same time, other low-bandwidth devices (such as smart bulbs, sensors, etc.) have very little demand for bandwidth. However, the existing adaptive bandwidth allocation technology based on network traffic prediction relies on historical data and device types to predict bandwidth requirements when allocating bandwidth, but usually fails to take into account the demand differences between high-bandwidth and low-bandwidth devices. The traffic prediction model cannot accurately identify which devices need more bandwidth, especially when multiple devices are working at the same time. It fails to prioritize the allocation of sufficient bandwidth to high-definition video surveillance cameras, resulting in the bandwidth requirements of these high-bandwidth devices not being met, thus affecting the quality of the video stream. Since the traffic prediction model fails to accurately distinguish the bandwidth demand priorities between devices, low-bandwidth devices such as smart bulbs may get too much bandwidth, while high-definition video surveillance may experience freezes or image quality degradation due to insufficient bandwidth, seriously affecting the stability of the smart home system and the effect of home security monitoring. This not only wastes bandwidth resources, but also leads to a decline in the overall performance of the smart home system, which in turn affects the user experience and may cause home security issues or device failures.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide an adaptive bandwidth allocation method based on network traffic prediction to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: an adaptive bandwidth allocation method based on network traffic prediction, specifically comprising the following steps:

[0009] The network monitoring system is used to detect and obtain the operating status information of each bandwidth device in the smart home system in real time, and all bandwidth devices in the preset time range are screened out and marked as bandwidth devices to be analyzed;

[0010] Obtain bandwidth demand information of each bandwidth device to be analyzed in real time, analyze it, evaluate the bandwidth demand of each bandwidth device to be analyzed, and classify each bandwidth device to be analyzed into high-demand bandwidth device, medium-demand bandwidth device and low-demand bandwidth device according to the evaluation result;

[0011] According to the division results of each bandwidth device to be analyzed, a bandwidth allocation mechanism is constructed to adopt different bandwidth allocation measures for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively;

[0012] During the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time, and the information is analyzed to evaluate whether the bandwidth allocation degree of each bandwidth device to be analyzed by the bandwidth allocation mechanism meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation results;

[0013] Continuously monitor the bandwidth usage and network performance fluctuations of each bandwidth device to be analyzed, dynamically adjust the bandwidth allocation strategy based on real-time data, and dynamically adapt to changes in device bandwidth requirements.

[0014] Preferably, the bandwidth demand information of each bandwidth device to be analyzed is obtained in real time, and analyzed, the bandwidth demand of each bandwidth device to be analyzed is evaluated, and each bandwidth device to be analyzed is divided into a high-demand bandwidth device, a medium-demand bandwidth device, and a low-demand bandwidth device according to the evaluation result, specifically including the following steps:

[0015] Obtain bandwidth demand information of each bandwidth device to be analyzed in real time and pre-process it;

[0016] Extracting bandwidth demand intensity information and bandwidth consumption fluctuation information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, and analyzing the information to generate bandwidth load potential coefficients and bandwidth adaptation sensitivity coefficients of each bandwidth device to be analyzed;

[0017] A bandwidth demand assessment model is constructed for the bandwidth load potential coefficient and bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed, and a bandwidth demand assessment index for each bandwidth device to be analyzed is generated by weighted summation. After generation, the index is analyzed to evaluate the bandwidth demand of each bandwidth device to be analyzed, and each bandwidth device to be analyzed is divided into a high-demand bandwidth device, a medium-demand bandwidth device, and a low-demand bandwidth device according to the evaluation result.

[0018] Preferably, the logic for obtaining the bandwidth load potential coefficient and bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed is as follows:

[0019] Extract the bandwidth demand intensity information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, including the bandwidth of uploading data, the bandwidth of downloading data, and the fluctuation range of bandwidth consumption of each bandwidth device to be analyzed at different times within a period of time, and mark them as , and , Indicates bandwidth devices to be analyzed within a period of time The bandwidth for uploading data at any time, Indicates bandwidth devices to be analyzed within a period of time The bandwidth for downloading data at any time, Indicates bandwidth devices to be analyzed within a period of time The fluctuation range of bandwidth consumption at each moment, , , and are all positive integers;

[0020] Calculate the bandwidth load potential coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0021] ;

[0022] In the formula, For the The bandwidth load potential coefficient of the bandwidth device to be analyzed;

[0023] Extract the bandwidth consumption fluctuation information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, including the change rate of the bandwidth demand of each bandwidth device to be analyzed at different times within a period of time and the ratio of bandwidth consumption to its bandwidth demand, and mark them as and , Indicates bandwidth devices to be analyzed within a period of time The rate of change of bandwidth demand at any moment, Indicates bandwidth devices to be analyzed within a period of time The ratio of bandwidth consumption at a given moment to its bandwidth demand;

[0024] Calculate the bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0025] ;

[0026] In the formula, For the The bandwidth adaptation sensitivity coefficient of the bandwidth device to be analyzed.

[0027] Preferably, the bandwidth load potential coefficient of each bandwidth device to be analyzed is generated and bandwidth adaptation sensitivity coefficient Construct a bandwidth demand assessment model and generate the bandwidth demand assessment index of each bandwidth device to be analyzed through weighted summation , the specific calculation formula is: ,in and The bandwidth load potential coefficients of each bandwidth device to be analyzed and bandwidth adaptation sensitivity coefficient The non-zero weight coefficient of ;

[0028] Determine the preset bandwidth demand assessment index threshold range , and after determination, the bandwidth demand assessment index of each bandwidth device to be analyzed is generated Compare and evaluate the bandwidth requirements of each bandwidth device to be analyzed based on the comparison results, and divide each bandwidth device to be analyzed into high bandwidth demand devices, medium bandwidth demand devices and low bandwidth demand devices based on the evaluation results. The specific comparison, analysis and classification are as follows:

[0029] like , the bandwidth demand of the bandwidth device to be analyzed is low demand, and the bandwidth device to be analyzed is classified as a low demand bandwidth device;

[0030] like , the bandwidth demand of the bandwidth device to be analyzed is medium demand, and the bandwidth device to be analyzed is classified as a medium demand bandwidth device;

[0031] like The bandwidth demand of the bandwidth device to be analyzed is high, and the bandwidth device to be analyzed is classified as a high-demand bandwidth device.

[0032] Preferably, a bandwidth allocation mechanism is constructed according to the division results of each bandwidth device to be analyzed, specifically: according to the division results of low-demand bandwidth devices, medium-demand bandwidth devices and high-demand bandwidth devices, different bandwidth allocation parameters are set respectively to form a bandwidth allocation mechanism; the bandwidth allocation mechanism is based on the bandwidth demand evaluation index of each device and the bandwidth adaptability of the device, and automatically adjusts the bandwidth allocation method and priority through pre-set rules;

[0033] Different bandwidth allocation measures are taken for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively. Specifically: for high-demand bandwidth devices, the enhanced bandwidth allocation parameters in the bandwidth allocation mechanism are used to prioritize bandwidth allocation for high-demand bandwidth devices, increase the bandwidth allocation frequency and increase the bandwidth allocation amount; for medium-demand bandwidth devices, the standard bandwidth allocation parameters in the bandwidth allocation mechanism are maintained to ensure that the bandwidth requirements of medium-demand bandwidth devices are met; for low-demand bandwidth devices, the reduced bandwidth allocation parameters in the bandwidth allocation mechanism are used to reduce the bandwidth allocation frequency and reduce the bandwidth occupancy.

[0034] Preferably, during the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time, and the information is analyzed to evaluate whether the bandwidth allocation degree of each bandwidth device to be analyzed by the bandwidth allocation mechanism meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation result, which specifically includes the following steps:

[0035] During the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time and pre-processed;

[0036] Extracting allocation demand matching information and resource adaptability information from the pre-processed real-time network performance information of each bandwidth device to be analyzed, and analyzing them, respectively generating bandwidth allocation satisfaction coefficients and bandwidth resource adaptation indexes for each bandwidth device to be analyzed;

[0037] A bandwidth allocation evaluation model is constructed based on the bandwidth allocation satisfaction coefficient and bandwidth resource adaptation index of each bandwidth device to be analyzed. The allocation evaluation coefficient of each bandwidth device to be analyzed is generated by weighted summation. After the generation, an analysis is performed to evaluate whether the bandwidth allocation degree of the bandwidth allocation mechanism for each bandwidth device to be analyzed meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation results.

[0038] Preferably, the logic for obtaining the bandwidth allocation satisfaction coefficient and bandwidth resource adaptation index of each bandwidth device to be analyzed is as follows:

[0039] Extract the allocation demand matching information from the pre-processed real-time network performance information of each bandwidth device to be analyzed, including the bandwidth consumption, bandwidth demand, and the difference between the actual allocated bandwidth and the predicted bandwidth demand of each bandwidth device to be analyzed at different times during a period of time during the implementation of the bandwidth allocation mechanism, and mark them as , and , Indicates that during the implementation of the bandwidth allocation mechanism, Moment The bandwidth consumption of the bandwidth devices to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The bandwidth demand of the bandwidth devices to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The difference between the actual bandwidth allocated to the bandwidth device to be analyzed and the predicted bandwidth demand. , , and All are positive integers;

[0040] Calculate the bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0041] ;

[0042] In the formula, For the The bandwidth allocation satisfaction coefficient of the bandwidth device to be analyzed;

[0043] The resource adaptability information is extracted from the real-time network performance information of each bandwidth device to be analyzed after preprocessing, including the bandwidth acquisition rate of each bandwidth device to be analyzed at different times during a period of time during the implementation of the bandwidth allocation mechanism, the change rate of bandwidth demand, and the ratio of the actual bandwidth obtained to its bandwidth demand, and they are marked as , and , Indicates that during the implementation of the bandwidth allocation mechanism, Moment The rate at which the bandwidth device to be analyzed obtains bandwidth. Indicates that during the implementation of the bandwidth allocation mechanism, Moment The change rate of bandwidth demand of the bandwidth device to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The ratio of the actual bandwidth obtained by the bandwidth device to be analyzed to its bandwidth demand;

[0044] Calculate the bandwidth resource adaptation index of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0045] ;

[0046] In the formula, For the The bandwidth resource adaptation index of the bandwidth device to be analyzed.

[0047] Preferably, the bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed is generated and bandwidth resource adaptation index Construct a bandwidth allocation evaluation model and generate the allocation evaluation coefficients of each bandwidth device to be analyzed through weighted summation , the specific calculation formula is: ,in and The bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed is and bandwidth resource adaptation index The non-zero weight coefficient of ;

[0048] Determine the preset allocation evaluation coefficient threshold of each bandwidth device to be analyzed , and after determination, the allocation evaluation coefficients of each bandwidth device to be analyzed are generated Compare and evaluate whether the bandwidth allocation mechanism meets the expected demand for each bandwidth device to be analyzed based on the comparison results, and optimize the bandwidth allocation mechanism based on the evaluation results. The specific comparison and analysis is as follows:

[0049] like , the bandwidth allocation mechanism does not meet the expected demand for the bandwidth device to be analyzed, and the bandwidth allocation mechanism needs to be optimized, including: adjusting the bandwidth allocation priority to prioritize bandwidth allocation for devices with high demand; optimizing bandwidth demand prediction; dynamically adjusting the bandwidth allocation strategy to adjust the allocation frequency and allocation amount according to the bandwidth adaptability and actual bandwidth consumption of the bandwidth device to be analyzed;

[0050] like The bandwidth allocation mechanism satisfies the expected demand for the bandwidth device to be analyzed, and there is no need to optimize the bandwidth allocation mechanism.

[0051] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0052] 1. The present invention can accurately evaluate the bandwidth demand and resource adaptability of each bandwidth device to be analyzed by introducing a bandwidth demand evaluation model, combining the bandwidth allocation satisfaction coefficient and the bandwidth resource adaptation index. Through this evaluation, the system can dynamically identify the bandwidth demand priority of the device, especially when multiple devices are running in parallel, and can accurately distinguish the bandwidth demand of high-demand devices and low-demand devices. This refined priority division avoids the problem of unreasonable bandwidth allocation in the prior art, ensures that high-bandwidth demand devices can obtain sufficient bandwidth first when bandwidth is tight, thereby effectively improving the utilization efficiency of bandwidth resources and avoiding low-bandwidth devices from occupying too much bandwidth resources.

[0053] 2. The present invention can continuously adjust the bandwidth allocation strategy according to real-time data during the implementation of the bandwidth allocation mechanism by monitoring the bandwidth usage of the device and the fluctuation of network performance in real time, combined with the bandwidth demand prediction model. Especially when the bandwidth demand of the device changes greatly, the system can flexibly adjust the bandwidth allocation frequency and allocation amount to ensure that high-demand devices can always be met, while avoiding bandwidth waste caused by excessive allocation. Through this dynamic adjustment, it can effectively adapt to changes in the network environment, ensure that bandwidth resources are optimally configured under different network loads and device requirements, and improve the flexibility and reliability of the system.

[0054] 3. By introducing the bandwidth allocation evaluation coefficient and the bandwidth demand evaluation index threshold, the present invention enables the system to continuously evaluate the effectiveness of bandwidth allocation, promptly discover insufficient allocation, and adjust the bandwidth allocation mechanism. If the allocation evaluation coefficient is less than the preset threshold, the system will automatically optimize the bandwidth allocation mechanism, adjust the bandwidth allocation priority, optimize the bandwidth demand forecast, and adjust the bandwidth allocation strategy. This mechanism ensures that the device can always obtain reasonable bandwidth allocation in various network environments, thereby avoiding the degradation of image quality or freezes in high-bandwidth devices such as high-definition video surveillance due to insufficient bandwidth, and improving the stability of the system and user experience. Through this efficient and accurate bandwidth allocation and optimization, the smart home system can ensure the maximum utilization of bandwidth resources when multiple devices work in parallel, effectively avoiding the problems of bandwidth resource waste and performance degradation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a flow chart of the adaptive bandwidth allocation method based on network traffic prediction of the present invention. DETAILED DESCRIPTION

[0057] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0058] The present invention provides Figure 1 The adaptive bandwidth allocation method based on network traffic prediction shown in the figure specifically includes the following steps:

[0059] The network monitoring system is used to detect and obtain the operating status information of each bandwidth device in the smart home system in real time, and all bandwidth devices in the preset time range are screened out and marked as bandwidth devices to be analyzed;

[0060] In order to detect and obtain the operating status information of each bandwidth device in the smart home system in real time through the network monitoring system, network monitoring agents or sensors can be deployed on each bandwidth device. These agents collect key performance indicators such as network traffic, bandwidth usage, latency, packet loss rate, etc. of the device in real time, and send these data to the centralized monitoring platform. The monitoring platform can use network traffic analysis algorithms to process these data in real time and identify the status and performance indicators of the device in a timely manner. For example, through the SNMP protocol (Simple Network Management Protocol) or HTTP-based API calls, the device can transmit its real-time operating data to the monitoring system. This data includes but is not limited to information such as bandwidth consumption, connection status, network load, etc. of the device. The network monitoring system can also integrate automatic monitoring functions to issue alarms when the device status is abnormal (such as excessive bandwidth usage, excessive latency, etc.), further enhancing the real-time and accuracy of monitoring.

[0061] Filtering out all bandwidth devices within a preset time range can be achieved by setting up a time filtering function in the monitoring system. For example, the system can set a time interval (such as a specific time period on weekdays or a time period commonly used by family members) and compare it with the real-time operation data of the device. If the operation time of the device fits this time range, the system will mark it as a "bandwidth device to be analyzed." This process can be achieved by recording the active timestamp of each device during data collection and performing a time range query in the monitoring platform. In addition, the monitoring system can integrate a time period filtering function to mark the active time of the device, so that only the devices that are active within a specific time period can be focused on during analysis. In terms of specific implementation, the database query function, such as the "WHERE" condition in the SQL statement, can be used to filter out devices within the preset time range for subsequent processing.

[0062] By obtaining the operating status information of the device through the real-time monitoring system and filtering out the devices to be analyzed according to the preset time range, the system can ensure that it can accurately capture the bandwidth requirements of different devices in the smart home environment and ensure the timeliness and accuracy of the analysis results. In the smart home system, the bandwidth requirements of different devices may vary over time and usage scenarios. For example, multiple high-definition video surveillance cameras may have completely different bandwidth requirements at different time periods when family members are out or at home. By accurately filtering out active devices, the system can avoid interference from invalid data and conduct more accurate bandwidth requirement assessments for high-bandwidth demand devices (such as video surveillance devices) and low-bandwidth demand devices (such as smart light bulbs), thereby reasonably allocating bandwidth resources, avoiding low-bandwidth devices from occupying too many resources and affecting the normal operation of high-bandwidth devices, and ultimately improving system stability and user experience.

[0063] Obtain bandwidth demand information of each bandwidth device to be analyzed in real time, analyze it, evaluate the bandwidth demand of each bandwidth device to be analyzed, and classify each bandwidth device to be analyzed into high-demand bandwidth device, medium-demand bandwidth device and low-demand bandwidth device according to the evaluation result;

[0064] In this embodiment, the bandwidth demand information of each bandwidth device to be analyzed is obtained in real time, and is analyzed to evaluate the bandwidth demand of each bandwidth device to be analyzed, and each bandwidth device to be analyzed is divided into a high-demand bandwidth device, a medium-demand bandwidth device, and a low-demand bandwidth device according to the evaluation result, specifically including the following steps:

[0065] Obtain bandwidth demand information of each bandwidth device to be analyzed in real time and pre-process it;

[0066] In order to obtain the bandwidth demand information of each bandwidth device to be analyzed in real time, it can usually be achieved through the data acquisition module in the network monitoring system. Specifically, the network monitoring system can integrate network traffic monitoring protocols (such as SNMP, NetFlow or sFlow) to capture the traffic data of the device in real time. The system will obtain key data such as the device's upstream traffic (upload bandwidth), downstream traffic (download bandwidth) and network load through these protocols. In addition, a timed polling mechanism can be used to extract traffic data from the device at a certain time interval (such as every second or every minute) and upload the data to a centralized monitoring platform in real time. For devices that support API interfaces, the system can also obtain bandwidth demand information by calling the device's network interface or through real-time streaming data transmission protocols (such as HTTP, MQTT). In this way, the system can obtain the bandwidth demand data of each device in a real-time environment for subsequent analysis and processing.

[0067] Data preprocessing is performed to ensure that the bandwidth demand information obtained from the device is of high quality and consistency in subsequent analysis. Since there may be data noise, packet loss, delay, and data format differences between different devices in the network environment, preprocessing can improve the accuracy and reliability of the data. The main steps of preprocessing include denoising, data cleaning, normalization, and data completion. First, denoising can reduce the noise caused by network fluctuations, packet loss, etc. through filters or sliding average algorithms. Secondly, data cleaning is to remove outliers, null values, or duplicate data. For example, when the traffic data of some devices increases abnormally or is zero, a threshold can be set to filter these unreasonable data points. Next, the normalization step adjusts the bandwidth demand data of different devices to a unified scale to ensure the comparability of data between different devices and avoid affecting the evaluation results due to inconsistent data ranges. Finally, if missing data occurs, data completion techniques (such as linear interpolation or mean filling) can be used to fill in the missing data to maintain data integrity and consistency. Through these preprocessing steps, the quality of the data will be guaranteed, which will help generate more accurate evaluation results in the subsequent analysis stage.

[0068] Extracting bandwidth demand intensity information and bandwidth consumption fluctuation information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, and analyzing the information to generate bandwidth load potential coefficients and bandwidth adaptation sensitivity coefficients of each bandwidth device to be analyzed;

[0069] In order to extract the bandwidth demand intensity information and bandwidth consumption fluctuation information of each bandwidth device to be analyzed after preprocessing, data analysis and feature extraction methods can be used. First, for bandwidth demand intensity information, it can be extracted by calculating the instantaneous uplink traffic and instantaneous downlink traffic of the device. These two data items reflect the bandwidth consumption intensity of the device in a specific time period, and the bandwidth demand intensity of each device can be extracted by simple statistical methods (such as summation and average value). For example, the bandwidth sum or average value of the device can be calculated within a specified time window to obtain the bandwidth demand intensity of the device. For bandwidth consumption fluctuation information, it can be extracted by calculating the standard deviation or fluctuation index of the device bandwidth usage. This requires analyzing the bandwidth usage fluctuation of the device within a certain time range from the preprocessed data. In specific implementation, the sliding window method can be used to segment the bandwidth consumption, and the standard deviation of bandwidth usage can be calculated within each time window. The larger the standard deviation, the greater the fluctuation of the bandwidth consumption of the device, and the more significant the corresponding fluctuation information. Through these methods, the system can accurately extract the bandwidth demand intensity and fluctuation information, providing basic data for subsequent bandwidth demand evaluation.

[0070] A bandwidth demand assessment model is constructed for the bandwidth load potential coefficient and bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed, and a bandwidth demand assessment index for each bandwidth device to be analyzed is generated by weighted summation. After generation, the index is analyzed to evaluate the bandwidth demand of each bandwidth device to be analyzed, and each bandwidth device to be analyzed is divided into a high-demand bandwidth device, a medium-demand bandwidth device, and a low-demand bandwidth device according to the evaluation result.

[0071] In this embodiment, the logic for obtaining the bandwidth load potential coefficient and bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed is as follows:

[0072] Extract the bandwidth demand intensity information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, including the bandwidth of uploading data, the bandwidth of downloading data, and the fluctuation range of bandwidth consumption of each bandwidth device to be analyzed at different times within a period of time, and mark them as , and , Indicates bandwidth devices to be analyzed within a period of time The bandwidth for uploading data at any time, Indicates bandwidth devices to be analyzed within a period of time The bandwidth for downloading data at any time, Indicates bandwidth devices to be analyzed within a period of time The fluctuation range of bandwidth consumption at each moment, , , and are all positive integers;

[0073] In order to obtain the bandwidth of upload data, download data and fluctuation range of bandwidth consumption of each bandwidth device to be analyzed at different times within a period of time in real time, network traffic monitoring tools and data collection systems can be deployed to collect network traffic data of devices in real time. In specific implementation, the system can send requests to each device regularly (such as every second or every minute) through protocols such as SNMP (Simple Network Management Protocol), NetFlow or sFlow to obtain the upload (upstream traffic) and download (downstream traffic) bandwidth data of the device. These data can be collected through the network interface of the device (such as routers or switches) and exported to the centralized monitoring platform through API interfaces or logs. On this basis, the fluctuation range of bandwidth consumption of the device can be obtained by analyzing its bandwidth changes over a period of time. The volatility is usually quantified by calculating the standard deviation of bandwidth consumption data. In order to ensure the real-time and accuracy of the data, the monitoring system will timestamp the data to ensure that the bandwidth consumption at each moment can be accurately traced in subsequent analysis, so as to extract the bandwidth demand information of each device for further analysis and evaluation.

[0074] Calculate the bandwidth load potential coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0075] ;

[0076] In the formula, For the The bandwidth load potential coefficient of the bandwidth device to be analyzed;

[0077] Calculate the bandwidth load potential coefficient of each bandwidth device to be analyzed Logarithmic operation, weighted summation and normalization are used to more accurately assess the bandwidth demand intensity and volatility of devices. First, logarithmic operation is used to process the uplink and downlink bandwidth ( and ), this operation method can effectively slow down the nonlinear growth of bandwidth demand as data increases, and avoid disproportionate impact when bandwidth demand increases suddenly. and Taking the logarithm ensures that the data remains stable and comparable even when it fluctuates over a large range. ) was standardized using The formula is designed to compress the impact of extreme fluctuations so that the impact of the fluctuation amplitude will not be overly amplified while maintaining the actual reflective ability of the data. This method ensures that even if the bandwidth demand of a device changes greatly at a certain moment, the entire evaluation process can still smooth out the fluctuations and highlight the long-term bandwidth demand trend of the device. By taking the weighted sum of the calculation results at all times, the bandwidth load potential coefficient of each device is obtained, which further provides a basis for comprehensive evaluation based on long-term bandwidth demand and volatility, thereby providing an accurate reference for bandwidth allocation.

[0078] No. The bandwidth load potential coefficient of the bandwidth device to be analyzed It reflects the intensity and volatility of the device's demand for bandwidth resources within a certain period of time. The size of is closely related to the bandwidth demand of the device. Specifically, a higher bandwidth load potential coefficient means that the device consumes more bandwidth during the time period and its bandwidth usage has significant fluctuations, which usually means that the device needs more bandwidth resources to maintain its normal operation. A higher value indicates that the device has a stronger demand for bandwidth and bandwidth must be allocated first. A value of 0 indicates that the bandwidth demand of the device is low and it can bear less bandwidth allocation when bandwidth resources are tight. Therefore, the size of the bandwidth load potential coefficient provides a quantitative basis for evaluating bandwidth demand. Devices with high values ​​will be classified as high-demand bandwidth devices to ensure that they are given priority in bandwidth allocation.

[0079] Extract the bandwidth consumption fluctuation information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, including the change rate of the bandwidth demand of each bandwidth device to be analyzed at different times within a period of time and the ratio of bandwidth consumption to its bandwidth demand, and mark them as and , Indicates bandwidth devices to be analyzed within a period of time The rate of change of bandwidth demand at any moment, Indicates bandwidth devices to be analyzed within a period of time The ratio of bandwidth consumption at a given moment to its bandwidth demand;

[0080] In order to obtain the bandwidth demand change rate and the ratio of bandwidth consumption to bandwidth demand of each bandwidth device to be analyzed at different times within a period of time in real time, the network monitoring system can be combined with traffic analysis tools to achieve this. First, the bandwidth demand change rate of the device can be achieved by real-time collection of the device's upstream and downstream traffic. The monitoring system uses SNMP, NetFlow or sFlow protocols to regularly obtain the device's traffic data and calculate the traffic difference at each moment to obtain the bandwidth demand change rate. For example, the system can calculate the difference in traffic at a set time interval (such as every second or every minute) to obtain the bandwidth change rate. For the ratio of bandwidth consumption to bandwidth demand, it can be calculated in real time by the ratio of bandwidth consumption (the actual bandwidth consumed by the device) to bandwidth demand (the device's predicted demand for bandwidth or set demand value). This can be monitored and dynamically calculated through preset thresholds or bandwidth scheduling algorithms to ensure that the system reflects the relationship between the device's bandwidth consumption and demand in real time. Through these methods, the monitoring system can accurately obtain the device's bandwidth change data and process it in real time to provide accurate input data for subsequent bandwidth adaptability evaluation.

[0081] Calculate the bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0082] ;

[0083] In the formula, For the The bandwidth adaptation sensitivity coefficient of the bandwidth device to be analyzed.

[0084] Calculate the bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed The method uses exponential calculation, normalization and weighted summation to more accurately evaluate the bandwidth demand adaptability of the device when the network load changes. First, the bandwidth demand change rate The exponential operation of is used to reflect the nonlinear response capability of the device to changes in bandwidth demand. The bandwidth demand change rate is divided by the bandwidth consumption fluctuation range ( ) to ensure that the impact of fluctuations on calculations is properly balanced and to avoid unreasonable calculation results caused by extreme fluctuations. Exponential function This makes the device's response to changes in bandwidth demand exponentially grow, meaning that the device's bandwidth adaptability is more sensitive when bandwidth demand changes greatly, thereby efficiently identifying the device's adaptability in a dynamic network environment.

[0085] Second, the ratio of bandwidth consumption to bandwidth demand ( ) is used to quantify the fluctuation of the device's bandwidth consumption relative to demand, and thus measure the device's adaptability under different bandwidth consumption. The standardized processing limits the fluctuation of bandwidth consumption of the device within a reasonable range, avoids excessive impact of excessive bandwidth consumption ratio on the final calculation result, and ensures the balance of device adaptability evaluation. Finally, the weighted summation processing method accumulates these different calculation results at the moment and averages them to obtain the final bandwidth adaptation sensitivity coefficient of each device.

[0086] No. The bandwidth adaptation sensitivity coefficient of the bandwidth device to be analyzed The size of is directly related to the bandwidth requirements of the device. Specifically, Devices with higher values ​​usually show stronger adaptability to bandwidth changes, which means that the device can flexibly adjust bandwidth consumption to meet high bandwidth demands when network load fluctuates. The higher the bandwidth adaptability sensitivity of a device, the more urgent its demand for bandwidth resources, especially when network load fluctuates greatly. Therefore, Devices with high bandwidth values ​​usually require more stable and sufficient bandwidth resources to cope with fluctuations during peak hours and when bandwidth demand changes, which makes their bandwidth demand correspondingly higher. Devices with lower values ​​are less adaptable to bandwidth fluctuations. Usually, their bandwidth demand is relatively stable, and it is suitable to allocate less bandwidth when bandwidth resources are tight. Therefore, the size of the bandwidth adaptation sensitivity coefficient is a key factor in evaluating the bandwidth demand of devices. Devices with high bandwidth requirements will be evaluated as devices with high bandwidth requirements and bandwidth resources will be allocated preferentially.

[0087] In this embodiment, the bandwidth load potential coefficient of each bandwidth device to be analyzed is generated. and bandwidth adaptation sensitivity coefficient Construct a bandwidth demand assessment model and generate the bandwidth demand assessment index of each bandwidth device to be analyzed through weighted summation , the specific calculation formula is: ,in and The bandwidth load potential coefficients of each bandwidth device to be analyzed and bandwidth adaptation sensitivity coefficient The non-zero weight coefficient of ;

[0088] In the bandwidth demand assessment model, the bandwidth demand assessment index of each bandwidth device to be analyzed is generated by weighted summation. The calculation formula is: ,in Indicates the device The bandwidth load potential coefficient of Indicates the device The bandwidth adaptation sensitivity coefficient. and is a non-zero weight coefficient used to weight these two coefficients, and satisfies the constraint , which means that the sum of the weights of these two coefficients is 1, thus ensuring that their proportions and impacts in the comprehensive assessment add up to a complete value. Specifically, The bandwidth load potential coefficient represents the importance of the evaluation. It can usually be set according to the traffic characteristics of the device or the role of the device in the network. If the device's demand for bandwidth resources fluctuates greatly over time, it may give Assign higher weight. Represents the weight of the bandwidth adaptation sensitivity coefficient, which usually measures the sensitivity and adaptability of the device when the bandwidth demand changes. If the device shows strong adaptability when the network load fluctuates, it may be increased accordingly. By properly setting these two weight coefficients, it is possible to properly balance the bandwidth load potential and the adaptability of the device in bandwidth demand assessment, thereby achieving more accurate bandwidth allocation.

[0089] Determine the preset bandwidth demand assessment index threshold range , and after determination, the bandwidth demand assessment index of each bandwidth device to be analyzed is generated Compare and evaluate the bandwidth requirements of each bandwidth device to be analyzed based on the comparison results, and divide each bandwidth device to be analyzed into high bandwidth demand devices, medium bandwidth demand devices and low bandwidth demand devices based on the evaluation results. The specific comparison, analysis and classification are as follows:

[0090] like , the bandwidth demand of the bandwidth device to be analyzed is low demand, and the bandwidth device to be analyzed is classified as a low demand bandwidth device;

[0091] This situation indicates that the bandwidth demand of the device is low. The system believes that the device has little demand for bandwidth and is usually in a low bandwidth consumption state. This means that the device has a relatively low demand for network bandwidth resources, and its bandwidth consumption fluctuates slightly, which will not cause a large pressure on the overall bandwidth allocation. Impact: When bandwidth resources are tight, the system can classify these devices as low-demand bandwidth devices and give them lower priority resources when allocating bandwidth to avoid wasting bandwidth. Such allocation helps ensure that more important high-demand devices can get enough bandwidth resources.

[0092] like , the bandwidth demand of the bandwidth device to be analyzed is medium demand, and the bandwidth device to be analyzed is classified as a medium demand bandwidth device;

[0093] This situation indicates that the bandwidth demand of the device is at a medium level, neither a low-demand device nor a high-demand device. This type of device usually has a certain demand for bandwidth during daily use, but will not cause serious pressure when bandwidth resources are scarce. Impact: The device is classified as a medium-demand bandwidth device. When allocating bandwidth, the system can allocate certain bandwidth resources based on real-time conditions to ensure that it can operate stably under normal circumstances, but it will not prioritize the allocation of too many resources. Such management can ensure the reasonable allocation of resources and avoid excessive bandwidth occupation.

[0094] like The bandwidth demand of the bandwidth device to be analyzed is high, and the bandwidth device to be analyzed is classified as a high-demand bandwidth device.

[0095] This situation indicates that the bandwidth demand of the device is very high and is usually in a high bandwidth consumption state. This means that when the network load is high, the device's demand for bandwidth increases sharply, and its bandwidth consumption may fluctuate greatly, indicating that it is highly dependent on bandwidth. Impact: The device will be classified as a high-demand bandwidth device, and the system should prioritize bandwidth resources to ensure that it can operate stably. Especially when bandwidth resources are tight, these high-demand devices need to be prioritized to avoid performance degradation or service interruption due to insufficient bandwidth.

[0096] Determining the pre-set bandwidth demand assessment index threshold interval can be achieved through a variety of data analysis methods, mainly relying on the analysis of historical data and the prediction of device performance requirements. First, the bandwidth demand of the device under different network loads can be understood through historical traffic data analysis. By collecting and analyzing the bandwidth demand assessment index of the device in the past period of time, the distribution of the bandwidth demand of the device can be calculated, and then the reasonable interval of the bandwidth demand assessment index can be determined. To achieve this, the software system can use statistical analysis methods, such as calculating the mean, standard deviation, quartiles of the data or using cluster analysis to identify the characteristics of devices with different bandwidth requirements. Through these statistical methods, the system can determine the minimum, maximum and middle interval of the bandwidth demand assessment index, thereby defining a suitable threshold interval. For example, by performing quantile analysis on historical data (such as the median and upper and lower quartiles), the distinction criteria for low-demand, medium-demand and high-demand devices can be set. In addition, the software system can also dynamically adjust these thresholds according to changes in device type, application scenario and network environment, to ensure that the bandwidth demand assessment index interval can adapt to changes in different time periods and devices, and ensure the flexibility and accuracy of bandwidth allocation.

[0097] According to the division results of each bandwidth device to be analyzed, a bandwidth allocation mechanism is constructed to adopt different bandwidth allocation measures for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively;

[0098] In this embodiment, a bandwidth allocation mechanism is constructed according to the division results of each bandwidth device to be analyzed, specifically: according to the division results of low-demand bandwidth devices, medium-demand bandwidth devices and high-demand bandwidth devices, different bandwidth allocation parameters are set respectively to form a bandwidth allocation mechanism; the bandwidth allocation mechanism is based on the bandwidth demand evaluation index of each device and the bandwidth adaptability of the device, and automatically adjusts the bandwidth allocation method and priority through pre-set rules;

[0099] In order to realize the construction of bandwidth allocation mechanism according to the division results of each bandwidth device to be analyzed, and to build a corresponding bandwidth allocation mechanism, a data-driven bandwidth management system can be used to achieve this. First, the system needs to collect the bandwidth demand information of each device in real time, and obtain the bandwidth usage of the device through network traffic monitoring tools (such as SNMP, NetFlow or sFlow). Then, the bandwidth demand evaluation index of each device is calculated through an algorithm model (such as the weighted sum method), and the devices are classified according to the set threshold (low demand, medium demand, high demand). Next, the system dynamically adjusts the bandwidth allocation strategy based on the classification results of the device and the preset bandwidth allocation rules. Specifically, for high-demand devices, the system can automatically allocate more bandwidth resources to them and increase the frequency of bandwidth allocation; for low-demand devices, the bandwidth allocation amount is reduced to give priority to the bandwidth use of high-demand devices. In addition, the system should have adaptive capabilities, that is, according to real-time data such as changes in network traffic and fluctuations in bandwidth demand, the priority and method of bandwidth allocation are automatically adjusted to ensure the optimal use of bandwidth resources. This mechanism can be achieved through real-time data monitoring, dynamic calculation and automatic scheduling functions to ensure that bandwidth allocation is both scientific and efficient.

[0100] Different bandwidth allocation measures are taken for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively. Specifically: for high-demand bandwidth devices, the enhanced bandwidth allocation parameters in the bandwidth allocation mechanism are used to allocate bandwidth to high-demand bandwidth devices first, increase the bandwidth allocation frequency and increase the bandwidth allocation amount, to ensure that the devices can maintain efficient operation under high load conditions and meet their high bandwidth needs; for medium-demand bandwidth devices, the standard bandwidth allocation parameters in the bandwidth allocation mechanism are maintained to ensure that the bandwidth needs of medium-demand bandwidth devices are met; for low-demand bandwidth devices, the reduced bandwidth allocation parameters in the bandwidth allocation mechanism are used to reduce the bandwidth allocation frequency and reduce the bandwidth occupancy, to ensure that these devices receive minimum bandwidth support when bandwidth resources are tight.

[0101] In order to implement different bandwidth allocation measures for high-demand, medium-demand, and low-demand bandwidth devices, bandwidth resources can be managed through a dynamic bandwidth allocation system. First, the system needs to monitor the bandwidth demand evaluation index of each device in real time and automatically adjust the bandwidth allocation strategy according to the bandwidth demand classification of the device (high demand, medium demand, and low demand). For high-demand bandwidth devices, the system will use enhanced bandwidth allocation parameters, that is, according to the high bandwidth demand of the device, increase the frequency of bandwidth allocation and increase the bandwidth allocation amount to ensure that the device can maintain a stable bandwidth supply under high load conditions. This strategy can give priority to meeting the bandwidth requests of high-demand devices by dynamically adjusting the bandwidth allocation upper limit and priority. For medium-demand bandwidth devices, the system will maintain standard bandwidth allocation parameters to ensure that the bandwidth demand of the device is basically met, but not too much bandwidth resources are occupied, so as to balance resource allocation and avoid resource waste. For low-demand bandwidth devices, the system uses reduced bandwidth allocation parameters to reduce the bandwidth allocation frequency and bandwidth occupancy, so as to ensure that low-demand devices can still obtain minimum bandwidth support when bandwidth resources are tight, but will not occupy bandwidth resources excessively. This strategy is implemented through bandwidth scheduling algorithms and bandwidth priority control, ensuring that bandwidth resources are dynamically allocated according to demand, so as to meet high-demand devices while not wasting bandwidth and optimizing the overall utilization of network resources.

[0102] During the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time, and the information is analyzed to evaluate whether the bandwidth allocation degree of each bandwidth device to be analyzed by the bandwidth allocation mechanism meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation results;

[0103] In this embodiment, during the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time, and the information is analyzed to evaluate whether the bandwidth allocation degree of each bandwidth device to be analyzed by the bandwidth allocation mechanism meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation result, which specifically includes the following steps:

[0104] During the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time and pre-processed;

[0105] During the implementation of the bandwidth allocation mechanism, real-time acquisition of network performance information of each bandwidth device to be analyzed can be achieved through network traffic monitoring tools (such as SNMP, NetFlow, sFlow and other protocols). These tools can collect information such as bandwidth usage, latency, packet loss rate, etc. from network devices in real time, and transmit the data to a centralized analysis platform. The acquired data usually needs to be preprocessed to improve data quality and ensure the accuracy of subsequent analysis. The preprocessing steps include: denoising, such as using the sliding average method to smooth data to reduce interference caused by network fluctuations or instantaneous traffic changes; standardization, by normalizing the data of different devices to ensure that the data of different devices can be effectively compared; filling missing values, using interpolation or mean filling and other technologies to complete the data loss caused by device failure or network interruption. The preprocessed data can ensure the accuracy of the bandwidth allocation model and make bandwidth allocation decisions more scientific and efficient.

[0106] Extracting allocation demand matching information and resource adaptability information from the pre-processed real-time network performance information of each bandwidth device to be analyzed, and analyzing them, respectively generating bandwidth allocation satisfaction coefficients and bandwidth resource adaptation indexes for each bandwidth device to be analyzed;

[0107] In order to extract the allocation demand matching information and resource adaptability information from the real-time network performance information of each bandwidth device to be analyzed after preprocessing, it can be achieved by combining the data analysis platform with the feature extraction algorithm. Specifically, first, the allocation demand matching information can be extracted by calculating the bandwidth demand difference of the device (that is, the difference between the actual bandwidth usage of the device and the predicted bandwidth demand). The system extracts the real-time bandwidth usage from the network performance data of the device and compares it with the bandwidth demand prediction value calculated based on historical data or traffic prediction model to obtain the bandwidth demand matching degree of the device. Secondly, the resource adaptability information can be extracted by analyzing the bandwidth allocation response speed and bandwidth usage fluctuation of the device. The specific method is that the system monitors the bandwidth allocation changes of the device in real time, calculates the change rate of bandwidth allocation of the device in different time periods, and further combines the bandwidth consumption fluctuation of the device to evaluate the adaptability of the device to bandwidth resources. These features can be automatically extracted through time series analysis or statistical analysis methods (such as standard deviation, rate of change, etc.) to ensure that the extracted features can effectively reflect the bandwidth demand and adaptability of the device. Through these methods, the system can accurately extract the allocation demand matching information and resource adaptability information, providing data support for subsequent bandwidth allocation evaluation.

[0108] A bandwidth allocation evaluation model is constructed based on the bandwidth allocation satisfaction coefficient and bandwidth resource adaptation index of each bandwidth device to be analyzed. The allocation evaluation coefficient of each bandwidth device to be analyzed is generated by weighted summation. After the generation, an analysis is performed to evaluate whether the bandwidth allocation degree of the bandwidth allocation mechanism for each bandwidth device to be analyzed meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation results.

[0109] In this embodiment, the logic for obtaining the bandwidth allocation satisfaction coefficient and bandwidth resource adaptation index of each bandwidth device to be analyzed is as follows:

[0110] Extract the allocation demand matching information from the pre-processed real-time network performance information of each bandwidth device to be analyzed, including the bandwidth consumption, bandwidth demand, and the difference between the actual allocated bandwidth and the predicted bandwidth demand of each bandwidth device to be analyzed at different times during a period of time during the implementation of the bandwidth allocation mechanism, and mark them as , and , Indicates that during the implementation of the bandwidth allocation mechanism, Moment The bandwidth consumption of the bandwidth devices to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The bandwidth demand of the bandwidth devices to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The difference between the actual bandwidth allocated to the bandwidth device to be analyzed and the predicted bandwidth demand. , , and All are positive integers;

[0111] In order to obtain the bandwidth consumption, bandwidth demand and the difference between the actual allocated bandwidth and the predicted bandwidth demand of each bandwidth device to be analyzed in real time, the network performance monitoring platform can be combined with the bandwidth demand prediction system. First, the bandwidth consumption (i.e. the real-time bandwidth usage of each device) can be obtained from the device or network switch / router in real time through protocols such as SNMP, NetFlow or sFlow. The system will regularly query the real-time traffic data of the network device and record the upload and download bandwidth of the device. Secondly, the bandwidth demand can be calculated by the historical usage data of the device and the real-time traffic prediction model. The model predicts the bandwidth demand of the device at the current moment through machine learning or statistical methods based on factors such as the device's past bandwidth usage pattern, network load and time period. Finally, the difference between the actual allocated bandwidth and the predicted bandwidth demand can be obtained by comparing the actual bandwidth allocated to the device (the allocation data controlled by the bandwidth management system) with its predicted bandwidth demand. These data can be transmitted to the centralized management platform through the API interface or real-time data stream, and calculated by the automated data processing module to update the bandwidth status of the device in real time. Through this system, real-time monitoring and data acquisition of bandwidth consumption, demand and allocation of each device can be ensured, thereby providing timely and accurate data support for the optimization of bandwidth allocation mechanism.

[0112] Calculate the bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0113] ;

[0114] In the formula, For the The bandwidth allocation satisfaction coefficient of the bandwidth device to be analyzed;

[0115] Bandwidth allocation satisfaction coefficient The calculation formula ensures that the effect of device bandwidth allocation can be accurately measured by comprehensively evaluating the device's bandwidth consumption, bandwidth demand, and allocation differences. First, Calculated the The device in The difference between the bandwidth consumption and bandwidth demand at a certain moment indicates the deviation between the actual bandwidth demand of the device and the predicted demand. The larger the difference, the lower the accuracy of the bandwidth allocation mechanism, so a higher weight should be given. Bandwidth allocation differences ( ) to avoid disproportionate impact of excessive values ​​on the results. Bandwidth allocation differences reflect the deviation of devices in actual bandwidth allocation. Standardization makes its impact adapt to bandwidth fluctuations of different devices and times, ensuring the stability of the system. Finally, the bandwidth allocation satisfaction coefficient of the device as a whole is obtained by averaging the bandwidth allocation satisfaction at all times through weighted summation. The advantage of this calculation is that it can comprehensively evaluate the bandwidth allocation of each device in different time periods, not only considering the matching degree of bandwidth allocation with demand, but also balancing the ability of the device to adapt to bandwidth allocation fluctuations, thereby providing a more accurate basis for bandwidth optimization.

[0116] No. Bandwidth allocation satisfaction coefficient of the bandwidth device to be analyzed It has a direct relationship with evaluating whether the bandwidth allocation mechanism meets the expected demand for the device bandwidth. Specifically, The higher the value, the smaller the gap between the bandwidth demand of the device and the actual allocated bandwidth, the better the accuracy and adaptability of bandwidth allocation, the actual bandwidth usage of the device is close to its predicted bandwidth demand, and the allocation mechanism meets the bandwidth demand of the device. A higher value indicates that the bandwidth allocation mechanism is more successful and can accurately identify and meet the bandwidth needs of devices. A low value indicates that the bandwidth allocation mechanism is not effective, and the bandwidth allocation of the device fails to meet the expected demand. There may be insufficient or over-allocation of bandwidth.

[0117] The resource adaptability information is extracted from the real-time network performance information of each bandwidth device to be analyzed after preprocessing, including the rate at which each bandwidth device to be analyzed obtains bandwidth at different times during a period of time during the implementation of the bandwidth allocation mechanism, the rate of change of bandwidth demand, and the ratio of the actual bandwidth obtained to its bandwidth demand, and they are marked as , and , Indicates that during the implementation of the bandwidth allocation mechanism, Moment The rate at which the bandwidth device to be analyzed obtains bandwidth. Indicates that during the implementation of the bandwidth allocation mechanism, Moment The change rate of bandwidth demand of the bandwidth device to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The ratio of the actual bandwidth obtained by the bandwidth device to be analyzed to its bandwidth demand;

[0118] In order to obtain the bandwidth acquisition rate, bandwidth demand change rate, and the ratio of actual bandwidth to bandwidth demand of each bandwidth device to be analyzed in real time, it can be achieved by deploying a network traffic monitoring system and a bandwidth demand prediction model. First, the bandwidth acquisition rate (i.e., the rate at which the device obtains bandwidth) can be obtained by regularly obtaining the upstream and downstream bandwidth traffic data of the device through network protocols such as NetFlow, sFlow, or SNMP, calculating the bandwidth growth rate at each moment, and reflecting the bandwidth acquisition of the device in real time. Secondly, the change rate of bandwidth demand can be calculated by a bandwidth demand prediction model based on historical data. The model analyzes the bandwidth usage pattern of the device in the past, combines factors such as the current network load and time period, predicts the bandwidth demand of the device, and calculates the change rate. Finally, the ratio of actual bandwidth to bandwidth demand can be calculated by comparing the bandwidth consumption and bandwidth demand of the device, tracking the actual bandwidth allocation of the device in real time and comparing it with the predicted value to obtain the ratio. By transmitting these data to a centralized management platform in real time and using data analysis tools for processing and calculation, the bandwidth allocation effect of each device can be accurately evaluated to ensure the reasonable allocation and optimization of bandwidth resources.

[0119] Calculate the bandwidth resource adaptation index of each bandwidth device to be analyzed. The specific calculation formula is as follows:

[0120] ;

[0121] In the formula, For the The bandwidth resource adaptation index of the bandwidth device to be analyzed.

[0122] Bandwidth resource adaptation index The calculation formula of the bandwidth acquisition rate of the device, the bandwidth demand change rate and the bandwidth resource utilization rate are comprehensively considered to accurately evaluate the adaptability of the device in the bandwidth allocation process and the utilization efficiency of bandwidth resources. Reflects the rate at which the device actually obtains bandwidth from the network during bandwidth allocation, using an exponential function Calculating it can more sensitively reflect the device's response to bandwidth changes. This exponential operation can enhance the effect of changes in bandwidth acquisition rate, making the device's response to bandwidth fluctuations more prominent, especially when bandwidth changes are large. Next, the rate of change in bandwidth demand By combining it with the acquisition rate in the formula, it ensures that the impact of bandwidth demand fluctuations is reasonably considered, avoiding excessive impact of extreme demand fluctuations on the bandwidth allocation mechanism. pass The expression is calculated based on the value of , which reflects the extent to which the device fails to effectively utilize resources in bandwidth allocation. The square of can better describe the ability of the device to adapt to bandwidth resources when the bandwidth is not fully utilized, and reduce its impact on the adaptation index under high utilization, ensuring that the device can maintain a high adaptation index when fully utilizing resources. Finally, by weighted summing and averaging the bandwidth adaptation degree at each moment, the overall bandwidth resource adaptation index of the device is obtained. , providing a basis for accurate optimization of bandwidth allocation.

[0123] No. The bandwidth resource adaptation index of the bandwidth device to be analyzed It is directly related to evaluating whether the bandwidth allocation mechanism meets the expected demand. A device with a higher value indicates that the matching degree between its bandwidth demand, bandwidth acquisition rate and bandwidth utilization is higher, and the device can effectively adapt to changes in the bandwidth allocation process and make full use of the allocated bandwidth resources. A device with a higher value usually indicates that its bandwidth allocation has approached or reached the expected demand and can operate stably without resource waste or bandwidth shortage. Devices with lower values ​​indicate that their bandwidth allocation fails to fully meet demand. There may be problems with insufficient bandwidth allocation or low resource utilization efficiency, indicating that the allocation mechanism fails to accurately identify device needs or fails to adapt to the characteristics of its bandwidth changes.

[0124] In this embodiment, the bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed is generated. and bandwidth resource adaptation index Construct a bandwidth allocation evaluation model and generate the allocation evaluation coefficients of each bandwidth device to be analyzed through weighted summation , the specific calculation formula is: ,in and The bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed is and bandwidth resource adaptation index The non-zero weight coefficient of ;

[0125] When constructing the bandwidth allocation evaluation model, the allocation evaluation coefficients of each bandwidth device to be analyzed are generated by weighted summation. , the specific calculation formula is: .in, Indicates the bandwidth allocation satisfaction coefficient, which measures the matching degree between the device bandwidth demand and the actual allocated bandwidth; It represents the bandwidth resource adaptation index, which measures the device's adaptability to bandwidth allocation and the efficiency of bandwidth resource utilization. By weighted summing these two indicators, we can comprehensively consider the bandwidth demand satisfaction and bandwidth adaptability of the device, and thus obtain a comprehensive evaluation coefficient.

[0126] Non-zero weight coefficient and They respectively represent the influence of bandwidth allocation satisfaction coefficient and bandwidth resource adaptation index on allocation evaluation coefficient. and has a non-zero value and satisfies , meaning that their sum is 1, ensuring that their contributions to the estimated coefficients are balanced in the overall model. and The specific value can be adjusted dynamically according to the actual application scenario and the characteristics of the device. If the system attaches more importance to the accuracy of bandwidth allocation, it can be improved If the system pays more attention to the effective use of bandwidth resources, it can increase In this way, the model can be flexibly adjusted to accurately reflect the comprehensive impact of the bandwidth allocation mechanism on each device.

[0127] Determine the preset allocation evaluation coefficient threshold of each bandwidth device to be analyzed , and after determination, the allocation evaluation coefficients of each bandwidth device to be analyzed are generated Compare and evaluate whether the bandwidth allocation mechanism meets the expected demand for each bandwidth device to be analyzed based on the comparison results, and optimize the bandwidth allocation mechanism based on the evaluation results. The specific comparison and analysis is as follows:

[0128] like , the bandwidth allocation mechanism for the bandwidth device to be analyzed does not meet the expected demand, and the bandwidth allocation mechanism needs to be optimized, including: adjusting the bandwidth allocation priority, giving priority to allocating bandwidth to devices with high demand, to ensure that their bandwidth demand is effectively met; optimizing bandwidth demand prediction, improving the accuracy of bandwidth allocation through a more accurate demand prediction model, and avoiding over-allocation and under-allocation; dynamically adjusting the bandwidth allocation strategy, adjusting the allocation frequency and allocation amount according to the bandwidth adaptability and actual bandwidth consumption of the bandwidth device to be analyzed, so as to achieve the optimal configuration of bandwidth resources;

[0129] This situation means that the bandwidth allocation mechanism fails to effectively meet the bandwidth needs of the device. This situation indicates that there is a large gap between the bandwidth demand of the device and the actual allocation, which may be because the bandwidth demand of the device is not accurately predicted or the allocation priority is not handled correctly. At this time, the system needs to optimize the bandwidth allocation mechanism.

[0130] To optimize the bandwidth allocation mechanism, data analysis and dynamic bandwidth management systems can be used to achieve this. Specific methods include: First, adjust the bandwidth allocation priority. By analyzing the bandwidth demand evaluation index of the device, devices with high demand can be automatically identified and bandwidth can be allocated to these devices first. By using the bandwidth priority algorithm, it can be ensured that when bandwidth resources are tight, the bandwidth demand of high-demand devices is prioritized to prevent the performance of key devices from being degraded due to insufficient bandwidth. Second, optimize bandwidth demand prediction. Machine learning models (such as regression analysis, time series prediction, or deep learning models) can be used to accurately predict the bandwidth demand of devices. By learning historical data and real-time traffic patterns, future bandwidth demand can be predicted to avoid imbalances in bandwidth allocation due to over-prediction or insufficient demand. Finally, dynamically adjust the bandwidth allocation strategy. By monitoring the bandwidth usage, bandwidth demand changes, and adaptability of the device in real time, the system can automatically adjust the frequency and amount of bandwidth allocation. For example, if the bandwidth consumption of the device fluctuates greatly, the system can increase the frequency of bandwidth adjustment to ensure that its bandwidth demand is met at any time; if the device is highly adaptable to bandwidth changes, the frequency of bandwidth adjustment can be reduced to reduce network overhead. These measures can be implemented through adaptive scheduling algorithms and real-time data feedback mechanisms in the bandwidth management platform to ensure efficient use of bandwidth resources and maximize device performance.

[0131] like The bandwidth allocation mechanism satisfies the expected demand for the bandwidth device to be analyzed, and there is no need to optimize the bandwidth allocation mechanism.

[0132] This indicates that the bandwidth allocation mechanism has effectively met the bandwidth needs of the device, and the gap between the actual bandwidth usage of the device and the expected needs is small. At this point, the device can obtain sufficient bandwidth resources during the bandwidth allocation process, and the allocation mechanism can accurately identify and meet the device needs, so no optimization is required. This shows that the system's bandwidth allocation strategy is successful and can ensure that the device operates efficiently in the network environment without causing resource waste or insufficient bandwidth. Maintaining this state, the system can continue to operate stably and ensure that the bandwidth needs of the device are always properly met during future allocation processes.

[0133] To determine the pre-set allocation evaluation coefficient thresholds of each bandwidth device to be analyzed, a data-driven analysis method can be used. First, the system can analyze the historical bandwidth usage data and allocation evaluation coefficient of the device, and use statistical analysis (such as mean, standard deviation, quartile, etc.) to determine the fluctuation range of the bandwidth demand of the device, so as to set a reasonable threshold interval. Specifically, the system can perform cluster analysis on historical data, divide the devices into different categories (such as low-demand, medium-demand, and high-demand devices), and set different thresholds based on the bandwidth allocation satisfaction and adaptability of each type of device. Secondly, a machine learning algorithm is used to train the prediction model of the device bandwidth demand, and automatically adjust the threshold in combination with the actual use of the device to make it more accurately reflect the changes in the bandwidth demand of the device. Finally, the system can combine real-time network status data to regularly adjust the threshold to adapt to changes in the network environment and ensure that the bandwidth allocation mechanism is always consistent with the actual needs of the device. Through these methods, the allocation evaluation coefficient threshold of each device can be dynamically determined and optimized to ensure the accuracy and flexibility of the bandwidth allocation mechanism.

[0134] Continuously monitor the bandwidth usage and network performance fluctuations of each bandwidth device to be analyzed, dynamically adjust the bandwidth allocation strategy based on real-time data, and dynamically adapt to changes in device bandwidth requirements.

[0135] To continuously monitor the bandwidth usage and network performance fluctuations of each bandwidth device to be analyzed and dynamically adjust the bandwidth allocation strategy, this can be achieved through a real-time data collection and analysis platform combined with an adaptive bandwidth scheduling algorithm. First, use a network monitoring system (such as SNMP, NetFlow, sFlow, etc.) to collect the bandwidth usage data of the device in real time, including key network performance indicators such as upload and download bandwidth, latency, and packet loss rate. The system can obtain this data regularly or in real time, and monitor the bandwidth consumption and network performance fluctuations of the device through data flow analysis tools. Secondly, based on the collected real-time data, use machine learning algorithms or statistical methods (such as time series analysis, sliding average, etc.) to predict the future bandwidth demand fluctuations of the device, and dynamically adjust the bandwidth allocation strategy based on these prediction results. Specifically, the system can automatically adjust the bandwidth allocation frequency and allocation amount according to the actual bandwidth usage of the device and the fluctuation of network performance, such as quickly increasing the bandwidth allocation when the bandwidth demand of the device increases, and reducing the bandwidth allocation when the demand decreases. Through this dynamic adaptation mechanism, it can ensure that bandwidth resources are reasonably allocated, improve bandwidth utilization, reduce network congestion, and ensure that the device can always obtain appropriate bandwidth support under different network loads and environmental changes. This approach can ensure the flexibility and efficiency of the system and ensure that bandwidth allocation is highly consistent with device requirements.

[0136] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0137] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0138] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0139] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0140] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An adaptive bandwidth allocation method based on network traffic prediction, characterized in that: The specific steps include: The network monitoring system is used to detect and obtain the operating status information of each bandwidth device in the smart home system in real time, and all bandwidth devices in the preset time range are screened out and marked as bandwidth devices to be analyzed; Obtain bandwidth demand information of each bandwidth device to be analyzed in real time, analyze it, evaluate the bandwidth demand of each bandwidth device to be analyzed, and classify each bandwidth device to be analyzed into high-demand bandwidth device, medium-demand bandwidth device and low-demand bandwidth device according to the evaluation result; The specific steps include: Obtain bandwidth demand information of each bandwidth device to be analyzed in real time and pre-process it; Extracting bandwidth demand intensity information and bandwidth consumption fluctuation information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, and analyzing the information to generate bandwidth load potential coefficients and bandwidth adaptation sensitivity coefficients of each bandwidth device to be analyzed; The logic for obtaining the bandwidth load potential coefficient and bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed is as follows: Extract the bandwidth demand intensity information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, including the bandwidth of uploading data, the bandwidth of downloading data, and the fluctuation range of bandwidth consumption of each bandwidth device to be analyzed at different times within a period of time, and mark them as , and , Indicates bandwidth devices to be analyzed within a period of time The bandwidth for uploading data at any time, Indicates bandwidth devices to be analyzed within a period of time The bandwidth for downloading data at any time, Indicates bandwidth devices to be analyzed within a period of time The fluctuation range of bandwidth consumption at each moment, , , and All are positive integers; Calculate the bandwidth load potential coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows: In the formula, For the The bandwidth load potential coefficient of the bandwidth device to be analyzed; Extract the bandwidth consumption fluctuation information from the pre-processed bandwidth demand information of each bandwidth device to be analyzed, including the change rate of the bandwidth demand of each bandwidth device to be analyzed at different times within a period of time and the ratio of bandwidth consumption to its bandwidth demand, and mark them as and , Indicates bandwidth devices to be analyzed within a period of time The rate of change of bandwidth demand at any moment, Indicates bandwidth devices to be analyzed within a period of time The ratio of bandwidth consumption at a given moment to its bandwidth demand; Calculate the bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows: In the formula, For the The bandwidth adaptation sensitivity coefficient of the bandwidth device to be analyzed; A bandwidth demand assessment model is constructed for the bandwidth load potential coefficient and bandwidth adaptation sensitivity coefficient of each bandwidth device to be analyzed, and a bandwidth demand assessment index of each bandwidth device to be analyzed is generated by weighted summation. After the generation, the bandwidth demand of each bandwidth device to be analyzed is analyzed to evaluate the bandwidth demand of each bandwidth device to be analyzed, and each bandwidth device to be analyzed is divided into a high-demand bandwidth device, a medium-demand bandwidth device, and a low-demand bandwidth device according to the evaluation result; According to the division results of each bandwidth device to be analyzed, a bandwidth allocation mechanism is constructed to adopt different bandwidth allocation measures for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively; During the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time, and the information is analyzed to evaluate whether the bandwidth allocation degree of each bandwidth device to be analyzed by the bandwidth allocation mechanism meets the expected demand, and the bandwidth allocation mechanism is optimized according to the evaluation results; The specific steps include: During the implementation of the bandwidth allocation mechanism, the real-time network performance information of each bandwidth device to be analyzed is obtained in real time and pre-processed; Extracting allocation demand matching information and resource adaptability information from the pre-processed real-time network performance information of each bandwidth device to be analyzed, and analyzing them, respectively generating bandwidth allocation satisfaction coefficients and bandwidth resource adaptation indexes for each bandwidth device to be analyzed; The logic for obtaining the bandwidth allocation satisfaction coefficient and bandwidth resource adaptation index of each bandwidth device to be analyzed is as follows: Extract the allocation demand matching information from the pre-processed real-time network performance information of each bandwidth device to be analyzed, including the bandwidth consumption, bandwidth demand, and the difference between the actual allocated bandwidth and the predicted bandwidth demand of each bandwidth device to be analyzed at different times during a period of time during the implementation of the bandwidth allocation mechanism, and mark them as , and , Indicates that during the implementation of the bandwidth allocation mechanism, Moment The bandwidth consumption of the bandwidth devices to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The bandwidth demand of the bandwidth devices to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The difference between the actual bandwidth allocated to the bandwidth device to be analyzed and the predicted bandwidth demand. , , and All are positive integers; Calculate the bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed. The specific calculation formula is as follows: In the formula, For the The bandwidth allocation satisfaction coefficient of the bandwidth device to be analyzed; The resource adaptability information is extracted from the real-time network performance information of each bandwidth device to be analyzed after preprocessing, including the bandwidth acquisition rate of each bandwidth device to be analyzed at different times during a period of time during the implementation of the bandwidth allocation mechanism, the change rate of bandwidth demand, and the ratio of the actual bandwidth obtained to its bandwidth demand, and they are marked as , and , Indicates that during the implementation of the bandwidth allocation mechanism, Moment The rate at which the bandwidth device to be analyzed obtains bandwidth. Indicates that during the implementation of the bandwidth allocation mechanism, Moment The change rate of bandwidth demand of the bandwidth device to be analyzed, Indicates that during the implementation of the bandwidth allocation mechanism, Moment The ratio of the actual bandwidth obtained by the bandwidth device to be analyzed to its bandwidth demand; Calculate the bandwidth resource adaptation index of each bandwidth device to be analyzed. The specific calculation formula is as follows: In the formula, For the The bandwidth resource adaptation index of the bandwidth device to be analyzed; The bandwidth allocation satisfaction coefficient for each bandwidth device to be analyzed and bandwidth resource adaptation index Construct a bandwidth allocation evaluation model and generate the allocation evaluation coefficients of each bandwidth device to be analyzed through weighted summation , the specific calculation formula is: ,in and The bandwidth allocation satisfaction coefficient of each bandwidth device to be analyzed is and bandwidth resource adaptation index The non-zero weight coefficient of ; Determine the preset allocation evaluation coefficient threshold of each bandwidth device to be analyzed , and after determination, the allocation evaluation coefficients of each bandwidth device to be analyzed are generated Compare and evaluate whether the bandwidth allocation mechanism meets the expected demand for each bandwidth device to be analyzed based on the comparison results, and optimize the bandwidth allocation mechanism based on the evaluation results. The specific comparison and analysis is as follows: like , the bandwidth allocation mechanism does not meet the expected demand for the bandwidth device to be analyzed, and the bandwidth allocation mechanism needs to be optimized, including: adjusting the bandwidth allocation priority to prioritize bandwidth allocation for devices with high demand; optimizing bandwidth demand prediction; dynamically adjusting the bandwidth allocation strategy to adjust the allocation frequency and allocation amount according to the bandwidth adaptability and actual bandwidth consumption of the bandwidth device to be analyzed; like , the bandwidth allocation mechanism satisfies the expected demand for the bandwidth device to be analyzed, and there is no need to optimize the bandwidth allocation mechanism; Continuously monitor the bandwidth usage and network performance fluctuations of each bandwidth device to be analyzed, dynamically adjust the bandwidth allocation strategy based on real-time data, and dynamically adapt to changes in device bandwidth requirements.

2. The adaptive bandwidth allocation method based on network traffic prediction according to claim 1 is characterized in that: The bandwidth load potential coefficient of each bandwidth device to be analyzed is generated and bandwidth adaptation sensitivity coefficient Construct a bandwidth demand assessment model and generate the bandwidth demand assessment index of each bandwidth device to be analyzed through weighted summation , the specific calculation formula is: ,in and The bandwidth load potential coefficients of each bandwidth device to be analyzed and bandwidth adaptation sensitivity coefficient The non-zero weight coefficient of ; Determine the preset bandwidth demand assessment index threshold range , and after determination, the bandwidth demand assessment index of each bandwidth device to be analyzed is generated Compare and evaluate the bandwidth requirements of each bandwidth device to be analyzed based on the comparison results, and divide each bandwidth device to be analyzed into high bandwidth demand devices, medium bandwidth demand devices and low bandwidth demand devices based on the evaluation results. The specific comparison, analysis and classification are as follows: like , the bandwidth demand of the bandwidth device to be analyzed is low demand, and the bandwidth device to be analyzed is classified as a low demand bandwidth device; like , the bandwidth demand of the bandwidth device to be analyzed is medium demand, and the bandwidth device to be analyzed is classified as a medium demand bandwidth device; like The bandwidth demand of the bandwidth device to be analyzed is high, and the bandwidth device to be analyzed is classified as a high-demand bandwidth device.

3. The adaptive bandwidth allocation method based on network traffic prediction according to claim 2 is characterized in that: According to the division results of each bandwidth device to be analyzed, a bandwidth allocation mechanism is constructed, specifically: according to the division results of low-demand bandwidth devices, medium-demand bandwidth devices and high-demand bandwidth devices, different bandwidth allocation parameters are set respectively to form a bandwidth allocation mechanism; the bandwidth allocation mechanism is based on the bandwidth demand evaluation index of each device and the bandwidth adaptability of the device, and automatically adjusts the bandwidth allocation method and priority through pre-set rules; Different bandwidth allocation measures are taken for high-demand bandwidth devices, medium-demand bandwidth devices and low-demand bandwidth devices respectively. Specifically: for high-demand bandwidth devices, the enhanced bandwidth allocation parameters in the bandwidth allocation mechanism are used to prioritize bandwidth allocation for high-demand bandwidth devices, increase the bandwidth allocation frequency and increase the bandwidth allocation amount; for medium-demand bandwidth devices, the standard bandwidth allocation parameters in the bandwidth allocation mechanism are maintained to ensure that the bandwidth requirements of medium-demand bandwidth devices are met; for low-demand bandwidth devices, the reduced bandwidth allocation parameters in the bandwidth allocation mechanism are used to reduce the bandwidth allocation frequency and reduce the bandwidth occupancy.

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